We introduce a technique of calibrating camera motions in basketball videos. Our method particularly transforms player positions to standard basketball court coordinates and enables applications such as tactical analysis and semantic basketball video retrieval. To achieve a robust calibration, we reconstruct the panoramic basketball court from a video, followed by warping the panoramic court to a standard one. As opposed to previous approaches, which individually detect the court lines and corners of each video frame, our technique considers all video frames simultaneously to achieve calibration; hence, it is robust to illumination changes and player occlusions. To demonstrate the feasibility of our technique, we present a stroke-based system that allows users to retrieve basketball videos. Our system tracks player trajectories from broadcast basketball videos. It then rectifies the trajectories to a standard basketball court by using our camera calibration method. Consequently, users can apply stroke queries to indicate how the players move in gameplay during retrieval. The main advantage of this interface is an explicit query of basketball videos so that unwanted outcomes can be prevented. We show the results in Figures 1, 7, 9, 10 and our accompanying video to exhibit the feasibility of our technique.

With the rapidly-increasing capacity demand over flash memory, 3D NAND flash memory has drawn tremendous attention as a promising solution to further reduce the bit cost and to increase the bit density. However, such advanced 3D devices will suffer more intensive program disturbance, compared to 2D NAND flash memory. Especially when multi-level-cell (MLC) technology is adopted, the deteriorated disturbance due to the program operations of intra and inter pages will become even more critical for reliability. In contrast to the past efforts that try to resolve the reliability issue with error correction codes or hardware designs, this work seeks for the redesign of the program operation. A disturb-aware programming scheme is proposed to not only relax the disturbance induced by slow cells as much as possible but also reduce the possibility in requiring a high voltage to program the slow cells. A series of experiments was conducted based on real 3D MLC flash chips, and the results demonstrate that the proposed scheme is extremely effective on reducing the disturbance as well as the bit error rate.

There is a growing demand to introduce more and more intelligence to storage devices in recent years, especially with the rapid increasing of hardware computing power. This paper exploits essential design issues in space utilization for dedup-based non-volatile phase-change memory (PCM). We explore the adoption of data duplication techniques to reduce potential data duplicates over PCM storage devices to provide more storage space than the physical storage space does. Among various data deduplication techniques, variable-sized chunking is considered in less cost-effective PCM-based storage devices because variable-sized chunking has better data deduplication capability than fixed-sized chunking. However, in a typical system architecture, data are written or updated in the fixed management units (e.g., LBAs). Thus, to ultimately improve the space utilization of PCM-based storage device, the technical problem falls on (1) how to map fixed-sized LBAs to variable-sized chunks and (2) how to efficiently manage (i.e., allocated and deallocate) free PCM storage space for variable-sized chunks. In this work, we propose a free space manager, called container-based space manager, to resolve the above two issues by exploiting the fact that (1) a storage system initially has more free space to relax the complexity on space management and (2) the space optimization of a storage system can grow with the time when it contains more and more data. The proposed design is evaluated over popular benchmarks, for which we have very encouraging results.

In this paper, we propose a new dictionary updating method for sparse dictionary learning. Our method imposes the $ell_0$ norm constraint on coefficients as well as a proximity regularization on the distance of dictionary modifications in the dictionary updating process. We show that the derived dictionary updating rule is a generalization of the K-SVD method. We study the convergence and the complexity of the proposed method. We also compare its performance with that of other methods.

In the aspect of a Demand-Side Platform (DSP), which is the agent of advertisers, we study how to predict the winning price such that the DSP can win the bid by placing a proper bidding value in the real-time bidding (RTB) auction. We propose to leverage the machine learning and statistical methods to train the winning price model from the bidding history. A major challenge is that a DSP usually suers from the censoring of the winning price, especially for those lost bids in the past. To solve it, we utilize the censored regression model, which is widely used in the survival analysis and econometrics, to t the censored bidding data. Note, however, the assumption of censored regression does not hold on the real RTB data. As a result, we further propose a mixture model, which combines linear regression on bids with observable winning prices and censored regression on bids with the censored winning prices, weighted by the winning rate of the DSP. Experiment results show that the proposed mixture model in general prominently outperforms linear regression in terms of the prediction accuracy.

Understanding the implications in smartphone usage and the power breakdown among hardware components has led to various energy-efficient designs for mobile systems. While energy consumption has been extensively explored, one critical dimension is often overlooked - unperceived activities that could steal a significant amount of energy behind users' back potentially. In this paper, we conduct the first exploration of unperceived activities in mobile systems. Specifically, we design a series of experiments to reveal, characterize, and analyze unperceived activities invoked by popular resident applications when an Android smartphone is left unused. We draw possible solutions inspired by the exploration and demonstrate that even an immediate remedy can mitigate energy dissipation to some extent.

We present the first efficient (i.e., polylogarithmic overhead) method for securely and privately processing large data sets over multiple parties with parallel, distributed algorithms. More specifically, we demonstrate load-balanced, statistically secure computation protocols for computing Parallel RAM (PRAM) programs, handling (1/3−) fraction malicious players, while preserving up to polylogarithmic factors the computation and memory complexities of the PRAM program, aside from a one-time execution of a broadcast protocol per party. Additionally, our protocol has communication locality—that is, each of the n parties speaks only with other parties.

Many enterprises or institutes are building private clouds within their own data centers. Data centers may have different batches of physical machines due to annual upgrades, but the number of machines is fixed most of the time. Consequently it is crucial to schedule jobs with different resource requirements and characteristics to meet different job timing constraints, in such heterogeneous yet most of the time static environments.
This paper describes a cloud resource management framework that dynamically allocates and reallocates computation resources for jobs that have different requirements, including deadline and priority. This framework makes decisions according to specified policies, and the framework provides four default policies for system administrators to choose to fit their specific needs. The framework is designed to be componentpluggable.
The components of the framework can be hotswapped, i.e., replaced without shutting down the services. In addition, the framework can work as an individual cloud computing system, or as an extension of an existing cloud system.
Our experiment results demonstrate that our system is capable of dynamically adjusting the resource allocation plan according to run-time statistics collected. The system also tolerates hardware failures, and will dynamically reallocate workers to compensate for the downtime in order to finish the jobs before deadline. Our experiments also suggest a trade-off between priority and deadline.

Metabolite identification remains a bottleneck in mass spectrometry (MS)-based metabolomics. Currently, this process relies heavily on tandem mass spectrometry (MS/MS) spectra generated separately for peaks of interest identified from previous MS runs. Such a delayed and labor-intensive procedure creates a barrier to automation. Further, information embedded in MS data has not been used to its full extent for metabolite identification. Multimers, adducts, multiply charged ions, and fragments of given metabolites occupy a substantial proportion (40-80%) of the peaks of a quantitation result. However, extensive information on these derivatives, especially fragments, may facilitate metabolite identification. We propose a procedure with automation capability to group and annotate peaks associated with the same metabolite in the quantitation results of opposite modes and to integrate this information for metabolite identification. In addition to the conventional mass and isotope ratio matches, we would match annotated fragments with low-energy MS/MS spectra in public databases. For identification of metabolites without accessible MS/MS spectra, we have developed characteristic fragment and common substructure matches. The accuracy and effectiveness of the procedure were evaluated using one public and two in-house liquid chromatography-mass spectrometry (LC-MS) data sets. The procedure accurately identified 89% of 28 standard metabolites with derivative ions in the data sets. With respect to effectiveness, the procedure confidently identified the correct chemical formula of at least 42% of metabolites with derivative ions via MS/MS spectrum, characteristic fragment, and common substructure matches. The confidence level was determined according to the fulfilled identification criteria of various matches and relative retention time.

The recent advances in imaging devices have opened the opportunity of better solving the tasks of video content analysis and understanding. Next-generation cameras, such as the depth or binocular cameras, capture diverse information, and complement the conventional 2D RGB cameras. Thus, investigating the yielded multimodal videos generally facilitates the accomplishment of related applications. However, the limitations of the emerging cameras, such as short effective distances, expensive costs, or long response time, degrade their applicability, and currently make these devices not online accessible in practical use. In this paper, we provide an alternative scenario to address this problem, and illustrate it with the task of recognizing human actions. In particular, we aim at improving the accuracy of action recognition in RGB videos with the aid of one additional RGB-D camera. Since RGB-D cameras, such as Kinect, are typically not applicable in a surveillance system due to its short effective distance, we instead offline collect a database, in which not only the RGB videos but also the depth maps and the skeleton data of actions are available jointly. The proposed approach can adapt the interdatabase variations, and activate the borrowing of visual knowledge across different video modalities. Each action to be recognized in RGB representation is then augmented with the borrowed depth and skeleton features. Our approach is comprehensively evaluated on five benchmark data sets of action recognition. The promising results manifest that the borrowed information leads to remarkable boost in recognition accuracy.

We present a novel two-pass framework for counting the number of people in an environment, where multiple cameras provide different views of the subjects. By exploiting the complementary information captured by the cameras, we can transfer knowledge between the cameras to address the difficulties of people counting and improve the performance. The contribution of this paper is threefold. First, normalizing the perspective of visual features and estimating the size of a crowd are highly correlated tasks. Hence, we treat them as a joint learning problem. The derived counting model is scalable and it provides more accurate results than existing approaches. Second, we introduce an algorithm that matches groups of pedestrians in images captured by different cameras. The results provide a common domain for knowledge transfer, so we can work with multiple cameras without worrying about their differences. Third, the proposed counting system is comprised of a pair of collaborative regressors. The first one determines the people count based on features extracted from intracamera visual information, whereas the second calculates the residual by considering the conflicts between intercamera predictions. The two regressors are elegantly coupled and provide an accurate people counting system. The results of experiments in various settings show that, overall, our approach outperforms comparable baseline methods. The significant performance improvement demonstrates the effectiveness of our two-pass regression framework.

In this paper, we present a clustering approach, MK-SOM, that carries out cluster-dependent feature selection, and partitions images with multiple feature representations into clusters. This work is motivated by the observations that human visual systems (HVS) can receive various kinds of visual cues for interpreting the world. Images identified by HVS as the same category are typically coherent to each other in certain crucial visual cues, but the crucial cues vary from category to category. To account for this observation and bridge the semantic gap, the proposed MK-SOM integrates multiple kernel learning (MKL) into the training process of self-organizing map (SOM), and associates each cluster with a learnable, ensemble kernel. Hence, it can leverage information captured by various image descriptors, and discoveries the cluster-specific characteristics via learning the per-cluster ensemble kernels. Through the optimization iterations, cluster structures are gradually revealed via the features specified by the learned ensemble kernels, while the quality of these ensemble kernels is progressively improved owing to the coherent clusters by enforcing SOM. Besides, MK-SOM allows the introduction of side information to improve performance, and it hence provides a new perspective of applying MKL to address both unsupervised and semi-supervised clustering tasks. Our approach is comprehensively evaluated in the two applications. The superior and promising results manifest its effectiveness

In this paper, we propose a novel multi-version B+-tree index structure, called block-based multi-version B+-tree (BbMVBT), for indexing multi-versions of data items in an embedded multi-version database (EMVDB) on flash memory. An EMVDB needs to support streams of update transactions and version-range queries to access different versions of data items maintained in the database. In BbMVBT, the index is divided into two levels. At the higher level, a multi-version index is maintained for keeping successive versions of each data item. These versions are allocated consecutively in a version block. At the lower level, a version array is used to search for a specific data version within a version block. With the reduced index structure of BbMVBT, the overhead for managing the index in processing update operations can be greatly reduced. At the same time, BbMVBT can also greatly reduce the number of accesses to the index in processing versionrange queries. To ensure sufficient free blocks for creating version blocks for efficient execution of BbMVBT, in this paper, we also discuss how to perform garbage collection using the purging-range queries for reclaiming “old” versions of data items and their associated entries in the index nodes. Analysis of the performance of BbMVBT is presented and verified with performance studies using both synthetic and real workloads. The performance results illustrate that BbMVBT can significantly improve the read and write performance to the multi-version index as compared with MVBT even though the sizes of the version blocks are not large.

Modeling the association between music and emotion has been considered important for music information retrieval and affective human computer interaction. This paper presents a novel generative model called acoustic emotion Gaussians (AEG) for computational modeling of emotion. Instead of assigning a music excerpt with a deterministic (hard) emotion label, AEG treats the affective content of music as a (soft) probability distribution in the valence-arousal space and parameterizes it with a Gaussian mixture model (GMM). In this way, the subjective nature of emotion perception is explicitly modeled. Specifically, AEG employs two GMMs to characterize the audio and emotion data. The fitting algorithm of the GMM parameters makes the model learning process transparent and interpretable. Based on AEG, a probabilistic graphical structure for predicting the emotion distribution from music audio data is also developed. A comprehensive performance study over two emotion-labeled datasets demonstrates that AEG offers new insights into the relationship between music and emotion (e.g., to assess the “affective diversity” of a corpus) and represents an effective means of emotion modeling. Readers can easily implement AEG via the publicly available codes. As the AEG model is generic, it holds the promise of analyzing any signal that carries affective or other highly subjective information.

Phosphaturic mesenchymal tumours (PMTs) are uncommon soft tissue and bone tumours that typically cause hypophosphataemia and tumour-induced osteomalacia (TIO) through secretion of phosphatonins including fibroblast growth factor 23 (FGF23). PMT has recently been accepted by the World Health Organization as a formal tumour entity. The genetic basis and oncogenic pathways underlying its tumourigenesis remain obscure. In this study, we identified a novel FN1–FGFR1 fusion gene in three out of four PMTs by next-generation RNA sequencing. The fusion transcripts and proteins were subsequently confirmed with RT-PCR and western blotting. Fluorescence in situ hybridization analysis showed six cases with FN1–FGFR1 fusion out of an additional 11 PMTs. Overall, nine out of 15 PMTs (60%) harboured this fusion. The FN1 gene possibly provides its constitutively active promoter and the encoded protein's oligomerization domains to overexpress and facilitate the activation of the FGFR1 kinase domain. Interestingly, unlike the prototypical leukaemia-inducing FGFR1 fusion genes, which are ligand-independent, the FN1–FGFR1 chimeric protein was predicted to preserve its ligand-binding domains, suggesting an advantage of the presence of its ligands (such as FGF23 secreted at high levels by the tumour) in the activation of the chimeric receptor tyrosine kinase, thus effecting an autocrine or a paracrine mechanism of tumourigenesis.

Improving the performance of storage systems without losing the reliability and sanity/integrity of file systems is a major issue in storage system designs. In contrast to existing storage architectures, we consider a PCM-based storage architecture to enhance the reliability of storage systems. In PCM-based storage systems, the major challenge falls on how to prevent the frequently updated (meta)data from wearing out their residing PCM cells without excessively searching and moving metadata around the PCM space and without extensively updating the index structures of file systems. In this work, we propose an adaptive wearleveling mechanism to prevent any PCM cell from being worn out prematurely by selecting appropriate data for swapping with constant search/sort cost. Meanwhile, the concept of indirect pointers is designed in the proposed mechanism to swap data without any modification to the file system’s indexes. Experiments were conducted based on well-known benchmarks and realistic workloads to evaluate the effectiveness of the proposed design, for which the results are encouraging.

The market trend of flash memory chips has been going for high density but low reliability. The rapidly increasing bit error rates and emerging reliability issues of the coming triple-level cell (TLC) and even three-dimensional (3D) flash chips would let users take an extremely high risk to store data in such low reliability storage media. With the observations in mind, this paper rethinks the layer design of flash devices and propose a complete paradigm shift to re-configure physical flash chips of potentially massive parallelism into better ??Virtual chips?? in order to improve the data recoverability in a modular and low-cost way. The concept of virtual chips is realized at hardware abstraction layer (HAL) without continually complicating the conventional flash management software of flash translation layer (FTL). The capability and compatibility of the proposed design are then both verified by a series of experiments with encouraging results.

Although the Multi-Level-Cell technique is widely adopted by flash-memory vendors to boost the chip density and to lower the cost, it results in serious performance and reliability problems. Different from the past work, a new cell programming method is proposed to not only significantly improve the chip performance but also reduce the potential bit error rate. In particular, a Single-Level-Cell-like programming style is proposed to better explore the threshold-voltage relationship to denote different Multi-Level-Cell bit information, which in turn drastically provides a larger window of threshold voltage similar to that found in Single-Level-Cell chips. It could result in less programming iterations and simultaneously a much less reliability problem in programming flash-memory cells. In the experiments, the new programming style could accelerate the programming speed up to 742% and even reduce the bit error rate up to 471% for Multi-Level-Cell pages.

The notion of zero-knowledge is formalized by requiring that for every malicious efficient verifierV* simulator S that can reconstruct the view of V* the prover, in a way that is indistinguishable to every polynomial-time distinguisher. Weak zero-knowledge weakens this notions by switching the order of the quantifiers and only requires that for every distinguisher D, there exists a (potentially different) simulatorSD.

In this paper we consider various notions of zero-knowledge, and investigate whether their weak variants are equivalent to their strong variants. Although we show (under complexity assumption) that for the standard notion of zero-knowledge, its weak and strong counterparts are not equivalent, for meaningful variants of the standard notion, the weak and strong counterparts are indeed equivalent. Towards showing these equivalences, we introduce new non-black-box simulation techniques permitting us, for instance, to demonstrate that the classical 2-round graph non-isomorphism protocol of Goldreich-Micali-Wigderson satisfies a “distributional” variant of zero-knowledge.

Our equivalence theorem has other applications beyond the notion of zero-knowledge. For instance, it directly implies the dense model theorem of Reingold et al (STOC ’08), and the leakage lemma of Gentry-Wichs (STOC ’11), and provides a modular and arguably simpler proof of these results (while at the same time recasting these result in the language of zero-knowledge).

We present a general framework and working system for predicting likely affective responses of the viewers in the social media environment after an image is posted online. Our approach emphasizes a mid-level concept representation, in which intended affects of the image publisher is characterized by a large pool of visual concepts (termed PACs) detected from image content directly instead of textual metadata, evoked viewer affects are represented by concepts (termed VACs) mined from online comments, and statistical methods are used to model the correlations among these two types of concepts. We demonstrate the utilities of such approaches by developing an end-to-end Assistive Comment Robot application, which further includes components for multi-sentence comment generation, interactive interfaces, and relevance feedback functions. Through user studies, we showed machine suggested comments were accepted by users for online posting in 90% of completed user sessions, while very favorable results were also observed in various dimensions (plausibility, preference, and realism) when assessing the quality of the generated image comments.

Modern processors are often enhanced using SIMD instructions, such as the MMX, SSE, and AVX instructions set in the x86 architecture, or the NEON instruction set in the ARM architecture. Using these SIMD instructions could significantly increase application performance, hence in application binaries a significant proportion of instructions are likely to be SIMD instructions. However, Dynamic Binary Translation (DBT) has largely overlooked SIMD instruction translation. For example, in the popular QEMU system emulator, guest SIMD instructions are often emulated with a sequence of scalar instructions even when the host machines have SIMD instructions to support such parallel computation, leaving significant potential for performance enhancement. In this paper, we propose two approaches, one leveraging the existing helper function implementation in QEMU, and the other using a newly introduced vector IR (Intermediate Representation) to enhance the performance of SIMD instruction translation in DBT of QEMU. Both approaches were implemented in the QEMU to support ARM and IA32 frontend and x86-64 backend. Preliminary experiments show that adding vector IR can significantly enhance the performance of guest applications containing SIMD instructions for both ARM and IA32 architectures when running with QEMU on the x86-64 platform.

Current Research Results

"A Virtual Repository for Linked-Data Based Disaster Management Applications," International Journal on Safety and Security Engineering, WIT Press, March 2015.

Typical state-of-the-art disaster management information systems (DMIS) cannot support responsive discovery and access of data and information needed to handle unforeseen emergencies. Adding semantics andrelations to legacy data and transforming them to linked data can remove this limitation. The virtual repository presented in this paper is a development environment for this purpose: It provides application developers with tools for incremental transformation of legacy data and information in the DMIS into linked data as needed by the applications. The virtual repository also provides the applications with support for runtime access of linked data created and maintained using its tools.

Current Research Results

"Design and Implementation of Participant Selection for Crowdsourcing Disaster Information," International Journal on Safety and Security Engineering, WIT Press, March 2015.

Authors: E. T.-H Chu, C.-Y. Lin, P. H. Tsai and J. W. S. Liu

Abstract:

Experiences with past major disasters tell us that people with wireless devices and social network services can serve effectively as mobile human sensors. A disaster warning and response system can solicit eye-witness reports from selected participants and use information provided by them to supplement surveillance sensor coverage. This paper first presents an overview the participant selection problem of how to select participants from available volunteers given the benefits and costs of deploying them. The greedy algorithm, named PSP-G, is known to be a near optimal solution with a small fraction of execution time when compared with well-known optimization methods. The paper then describes an implementation of the PSP-G algorithm and the integration of the PSP-G module into the Ushahidi platform. Performance data from two case studies, Haiti Earthquake 2010 and Typhoon Morakot 2009, also described here, clearly show that PSP-G is a general, practical solution.

We present a new and conceptually simpler proof of a tight parallel-repetition theorem for public-coin arguments [Pass-Venkitasubramaniam, STOC’07], [H˚astad et al, TCC’10], [Chung-Liu, TCC’10]. We follow the same proof framework as the previous non-tight parallel-repetition theorem of H˚astad et al—which relied on statistical distance to measure the distance between experiments—and show that it can be made tight (and further simplified) if instead relying on KL-divergence as the distance between the experiments.

We then use this new proof to present the first tight “Chernoff-type” parallel repetition theorem for arbitrary public-coin arguments, demonstrating that parallel-repetition can be used to simultaneously decrease both the soundness and completeness error of any public-coin argument at a rate matching the standard Chernoff bound.